Firebase vs. Google BigQuery

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Firebase
Score 8.4 out of 10
N/A
Google offers the Firebase suite of application development tools, available free or at cost for higher degree of usages, priced flexibly accorded to features needed. The suite includes A/B testing and Crashlytics, Cloud Messaging (FCM) and in-app messaging, cloud storage and NoSQL storage (Cloud Firestore and Firestore Realtime Database), and other features supporting developers with flexible mobile application development.
$0.01
Per Verification
Google BigQuery
Score 8.4 out of 10
N/A
Google's BigQuery is part of the Google Cloud Platform, a database-as-a-service (DBaaS) supporting the querying and rapid analysis of enterprise data.
$0.04
Pricing
FirebaseGoogle BigQuery
Editions & Modules
Phone Authentication
$0.01
Per Verification
Stored Data
$0.18
Per GiB
Standard edition
$0.04 / slot hour
Enterprise edition
$0.06 / slot hour
Enterprise Plus edition
$0.10 / slot hour
Offerings
Pricing Offerings
FirebaseGoogle BigQuery
Free Trial
NoYes
Free/Freemium Version
NoYes
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
FirebaseGoogle BigQuery
Considered Both Products
Firebase
Chose Firebase
Supabase is more user-friendly especially since it is directly integrated with Lovable, one of the most popular AI coder for non-technical users.
Chose Firebase
Supabase seems to have the best of all worlds right now. Followed by MongoDB/Firebase for smaller projects requiring less manpower and resources. Azure and Microsoft are reserved for existing projects and larger corporate clients.
Chose Firebase
Although there are other backend platforms that could have provided us with a solution to our project. The way of grouping the solution in FIREBASE, atomizing in the same project the database, cloud functions, authentication, push notifications, etc., has given us a clearer …
Chose Firebase
Unlike other tools in the GCP suite that have an equivalent in other clouds such as Bigquery (Athenas on AWS), AI Platform (Sagemaker), Storage (S3), we do not find an equivalent as complete as Firebase in any other provider. This is the main reason why we chose this provider …
Chose Firebase
Firebase came to a multiuse case for our product for authenticating backend services, users on the app & get data on the user base using the dashboard.
Chose Firebase
Firebase has a single NoSQL database, it is a simple, powerful and uniform application development platform in connectors, it has multiple programming languages ​​such as JavaScript and necessary tools that will simplify the creation of applications.
Chose Firebase
Firebase poses great documentation and integration with Android devices. And it's very good as well for iOS ones. So, for these scenarios, Firebase becomes the ideal ally.
Chose Firebase
It eases the app development process, has an extensive database that allows you to store media files in the cloud, supports robust uploads and downloads, and login authentication on any platform.
Chose Firebase
PostgreSQL is a relational database and is well suited for complex applications, however, Firebase is well suited for light and faster applications.
Chose Firebase
Firebase is easy to manage and scale really well for web application services. It offers better authentication and is easy to implement. For real-time analytics on web applications, it works very well. Firebase offers more features compared to other services especially it can …
Chose Firebase
  • User interface and console are very easy to understand
  • There is no pain in Integration with IOS, Android, and unity platforms
  • Cost-effective
Chose Firebase
We choose Firebase for various reasons:
  • A large set of resources and world-class Google support.
  • Pricing was a larger role in this decision.
Chose Firebase
If I get a suitable budget next year, I'll be switching us to Adobe Analytics. Their app/web tracking and the interface are streets ahead of Firebase.
Chose Firebase
Firebase is a much more comprehensive tool. While Fabric only had user traffic and trends data, it did not have the user communication set of tools. While CleverTap has CRM tools, it does not have tools for developers and product teams. While Adobe Analytics is good with …
Chose Firebase
I haven't played much with Heroku beyond deploying projects from Github. It looks to be very similar in providing a cloud-based platform for developing and deploying web apps as quickly as possible. I would look at comparing both of these before choosing a solution. I am just …
Chose Firebase
We chose Firebase for the following reasons.
  • Online resources.
  • Comes with better support.
Chose Firebase
Before using Firebase, we exclusively used self hosted database services. Using Firebase has allowed us to reduce reliance on single points of failure and systems that are difficult to scale. Additionally, Firebase is much easier to set up and use than any sort of self hosted …
Chose Firebase
Firebase does a lot of things well, but Branch.io does a lot of things great. We originally chose Firebase because it was free, had great crash reporting, and full event tracking. As we began to scale, increase paid marketing spend, and implement features such as journey …
Chose Firebase
It's tough to pick out competitors against Firebase as I'm really unsure and doubt there's another product exactly like it. As mentioned before Firebase literally does everything you can imagine for a mobile application but doesn't get insanely deep in one feature or action. It …
Chose Firebase
Firebase is well suited for projects with simpler database workloads that require its real-time features. For data that is heavily read in real time, it's a great choice and gives developers a lot of features that would have been complicated and time-consuming to build up front …
Google BigQuery
Chose Google BigQuery
is much better as it’s easily accessible provides velvet documentation and fulfils all our needs as well as easily integrated into clients, environment
Chose Google BigQuery
Google BigQuery is simpler and I say it has simpler UI too.
If you have a clear long term ask , mainly business intelligence needs then Google BigQuery offers you good.
If you need too much of features under a single cloud and you are ok to be lil clumsy then you can check …
Chose Google BigQuery
I have used most of the data analytics platforms. Based on my work, I have found that the user interface of Google BigQuery is simple to navigate. I like the front view - ease of joining tables, and integration with other platforms.
Chose Google BigQuery
Compared to every other analytics DB solution I've used, Google BigQuery was by far the easiest to set up and maintain, and scale.
The price was also much lower for our use case (internal data analysis).
Chose Google BigQuery
For our usage, Google BigQuery is cheaper and more performant. The others have their place, but in certain scenarios, Google BigQuery is a better solution.
Chose Google BigQuery
We actually use Snowflake and BigQuery in tandem because they both currently meet various needs. Redshift, however, has barely been used since our migration away from it. In the case of both Snowflake and BigQuery, they beat Redshift by a long shot. The main reasons are their …
Chose Google BigQuery
I came to use BigQuery from a traditional system like MS SQL server, the features which are available in BigQuery as a cloud service far outweigh the features from SQL server. I have not used other similar tools like Amazon Redshift but Google BigQuery serves multiple use cases …
Chose Google BigQuery
Google BigQuery is cheaper and much faster as compared to both. While as compared to Snowflake , we tested it was faster and cheaper by 30%, that is after Snowflake tweaked their environment, if not for that it would have been 90% cheaper than snowflake. Redshift is not easy …
Chose Google BigQuery
In my opinion, Google BigQuery is custom made to be the best data lake system that is easy to use, scalas to fit any business size, has inbuilt security, as well as tools for data integrity. Although a few other tools have some of the same functionality, Google BigQuery is the …
Chose Google BigQuery
It's easier to connect data between BigQuery and looker studio instead of connecting the data between BigQuery and tableau in terms of data explore or dashboard creating. Therefore we are considering migrating dashboards from tableau to looker studio for the whole company.
On …
Chose Google BigQuery
When comparing Google BigQuery and Databricks, both platforms are powerful tools for managing and analyzing large datasets. BQ is ideal for businesses requiring large-scale analytics, reporting, and dashboarding with minimal operational overhead. It’s also great for ad-hoc …
Chose Google BigQuery
Google BigQuery's main advantage over its direct competitors (Amazon Redshift and Azure Synapse) is that it is widely supported by non-Google software, while the others rely heavily on their own cloud ecosystems.
Chose Google BigQuery
I have used other data manipulation tools like SQL Server and Google BigQuery feels more intuitive, Google provides so much documentation and tutorials that getting to know the software is not only easy but even satisfactory, so I'd say Google BigQuery is very superior to that …
Chose Google BigQuery
Amazon Redshift was a likely alternative we were considering , but it needs to be provisioned on cluster and nodes, which increases infrastructure management, whereas Google BigQuery is serverless, so no infra management :) Also, I remember when comparing them we did found out …
Chose Google BigQuery
Its same as compared to Big query. We go with big query because of clients requirements in project.
Chose Google BigQuery
Google BigQuery as a platform allows for more integrations and customizability than many other offerings. Users mostly need to understand the basics of database and SQL programming in order to get the most from the product. However, other products like Hevo do have less of a …
Chose Google BigQuery
There are some areas in which this product is better while there are some in which others do better. It's not like Google BigQuery surpasses them in every metric. For a holistic view, I will say we use this because of - scalability, performance, ease of use, and seamless …
Chose Google BigQuery
The data performance of Google BigQuery is best as per other software. Limitations on Google BigQuery's data size are superior to those of Microsoft SQL. Obtaining real-time data from several IoT devices is another benefit.
Chose Google BigQuery
I personally find it by far simpler than Amazon redshift due it's onboarding seamlessness. For a quick start and simplify tye access to read the data big query provide better user experience and a smoother user interface. More importantly, the fact that Big Query can be easily …
Chose Google BigQuery
Compared to SingleStore, BigQuery has a big advantage of being completely serverless, and without practical limitations.

Compared to RedShift, we found the cost model to be more fitted to our needs.
Chose Google BigQuery
BigQuery can automatically scale to accommodate the data and query load, providing potentially unlimited scalability. At the same time, Redshift requires manual scaling efforts to increase or decrease capacity, which might affect performance during scaling operations.
Chose Google BigQuery
We focused more on data volume and less on full application capabilities. All in all, we found that the two solutions complement each other. For integration, some sources were better handled in SAP HANA, particularly other SAP systems where Google Big Query was more suitable …
Chose Google BigQuery
SingleStore has a much lower query latency compared to BigQuery. Thus, we segregate faster tasks to SingleStore, and use BigQuery has our main database to store all historical data.
Chose Google BigQuery
Google BigQuery i would say is better to use than AWS Redshift but not SQL products but this could be due to being more experience in Microsoft and AWS products. It would be really nice if it could use standard SQL server coding rather than having to learn another dialect of …
Chose Google BigQuery
First and foremost, Google BigQuery's pricing structure, based on data processing and storage, is more cost-effective for our needs. Secondly, since we already use other Google Cloud services, its tight integration with them especially, with Cloud Storage and Dataflow was a big …
Features
FirebaseGoogle BigQuery
Database-as-a-Service
Comparison of Database-as-a-Service features of Product A and Product B
Firebase
-
Ratings
Google BigQuery
8.4
Ratings
3% below category average
Automatic software patching00 Ratings8.00 Ratings
Database scalability00 Ratings9.20 Ratings
Automated backups00 Ratings8.50 Ratings
Database security provisions00 Ratings8.60 Ratings
Monitoring and metrics00 Ratings8.00 Ratings
Automatic host deployment00 Ratings8.00 Ratings
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FirebaseGoogle BigQuery
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Score 9.1 out of 10
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Score 7.4 out of 10
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Score 9.1 out of 10
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Enterprises
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User Ratings
FirebaseGoogle BigQuery
Likelihood to Recommend
7.0
(0 ratings)
8.6
(0 ratings)
Likelihood to Renew
-
(0 ratings)
8.1
(0 ratings)
Usability
7.0
(0 ratings)
7.7
(0 ratings)
Support Rating
7.3
(0 ratings)
7.3
(0 ratings)
User Testimonials
FirebaseGoogle BigQuery
Likelihood to Recommend
Firebase should be your first choice if your platform is mobile first. Firebase's mobile platform support for client-side applications is second to none, and I cannot think of a comparable cross-platform toolkit. Firebase also integrates well with your server-side solution, meaning that you can plug Firebase into your existing app architecture with minimal effort.
Firebase lags behind on the desktop, however. Although macOS support is rapidly catching up, full Windows support is a glaring omission for most Firebase features. This means that if your platform targets Windows, you will need to implement the client functionality manually using Firebase's web APIs and wrappers, or look for another solution.
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Google BigQuery is great for being the central datastore and entry point of data if you're on GCP. It seamlessly integrates with other Google products, meaning you can ingest data from other Google products with ease and little technical knowledge, and all of it is near real-time. Being serverless, BigQuery will scale with you, which means you don't have to worry about contention or spikes in demand/storage. This can, however, mean your costs can run away quickly or mount up at short notice.
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Pros
  • Extremely robust. Has about any tool you can think of under one roof making it extremely useful as a backup platform for data analytics or small teams that need something quickly.
  • Intuitive and easy UI/UX. Being made and owned by Google, you expect nothing less. Very easy to use for anyone that has any marketing or analytical experience especially in Google Analytics (which I just assume all marketers do).
  • Safe, secure, and sturdy. Never need to worry about downtimes or misinformation as it's as clean and safe as it is being run by Google.
  • FREE! What else is there to say. Unless you're an extremely large application handling hundreds of thousands to millions of users, this pay as you go plan will stay free.
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  • Its serverless architecture and underlying Dremel technology are incredibly fast even on complex datasets. I can get answers to my questions almost instantly, without waiting hours for traditional data warehouses to churn through the data.
  • Previously, our data was scattered across various databases and spreadsheets and getting a holistic view was pretty difficult. Google BigQuery acts as a central repository and consolidates everything in one place to join data sets and find hidden patterns.
  • Running reports on our old systems used to take forever. Google BigQuery's crazy fast query speed lets us get insights from massive datasets in seconds.
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Cons
  • Firebase/Firestore has very limited support for querying more complicated items; for example, performing a simple string search is not possible.
  • While upfront costs are low, costs can grow quickly if you're not careful about what you are being billed for.
  • Dashboards have at times shown different information to what is billed, and support from Google is less than stellar and not as effective as that from Amazon or Microsoft.
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  • It is challenging to predict costs due to BigQuery's pay-per-query pricing model. User-friendly cost estimation tools, along with improved budget alerting features, could help users better manage and predict expenses.
  • The BigQuery interface is less intuitive. A more user-friendly interface, enhanced documentation, and built-in tutorial systems could make BigQuery more accessible to a broader audience.
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Likelihood to Renew
No answers on this topic
We have to use this product as its a 3rd party supplier choice to utilise this product for their data side backend so will not be likely we will move away from this product in the future unless the 3rd party supplier decides to change data vendors.
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Usability
Firebase functions are more difficult to use, there are no concepts of triggers or cascading deletes without the use of Firebase functions. Firebase functions can run forever if not written correctly and cause billing nightmares. While this hasn't happened to us specifically it is a thing that happens more than one realizes.
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web UI is easy and convenient. Many RDBMS clients such as aqua data studio, Dbeaver data grid, and others connect. Range of well-documented APIs available. The range of features keeps expanding, increasing similar features to traditional RDBMS such as Oracle and DB2
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Reliability and Availability
No answers on this topic
I have never had any significant issues with Google Big Query. It always seems to be up and running properly when I need it. I cannot recall any times where I received any kind of application errors or unplanned outages. If there were any they were resolved quickly by my IT team so I didn't notice them.
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Performance
No answers on this topic
I think Google Big Query's performance is in the acceptable range. Sometimes larger datasets are somewhat sluggish to load but for most of our applications it performs at a reasonable speed. We do have some reports that include a lot of complex calculations and others that run on granular store level data that so sometimes take a bit longer to load which can be frustrating.
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Support Rating
Our analytics folks handled the majority of the communication when it came to customer service, but as far as I was aware, the support we got was pretty good. When we had an issue, we were able to reach out and get support in a timely fashion. Firebase was easy to reach and reasonably available to assist when needed.
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BigQuery can be difficult to support because it is so solid as a product. Many of the issues you will see are related to your own data sets, however you may see issues importing data and managing jobs. If this occurs, it can be a challenge to get to speak to the correct person who can help you.
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Alternatives Considered
Before using Firebase, we exclusively used self hosted database services. Using Firebase has allowed us to reduce reliance on single points of failure and systems that are difficult to scale. Additionally, Firebase is much easier to set up and use than any sort of self hosted database. This simplicity has allowed us to try features that we might not have based on the amount of work they required in the past.
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Google BigQuery of course collects a much much larger array of raw data and can handle (practically) an unlimited amount of data. For a large enterprise like ours that relies on large-scale analytics, this is absolutely imperative. Google BigQuery can also combine GA4 data with external sources (like CRM tools), so our analytics can be unified. Due to our heavy reliance on GA4, Google BigQuery is the natural choice since it is a Google product and has better integration.
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Scalability
No answers on this topic
We have continued to expand out use of Google Big Query over the years. I'd say its flexibility and scalability is actually quite good. It also integrates well with other tools like Tableau and Power BI. It has served the needs of multiple data sources across multiple departments within my company.
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Return on Investment
  • Firebase has been able to help us understand reliably, the drop-off in our user flows with their funnel feature. This has made it easy for us to be able to pinpoint weaknesses in our funnel and test and optimize with data as the dependent variable.
  • From an economic standpoint, we don't pay for Firebase which is great, but as the saying goes "You get what you pay for" also holds true in this context. As we looked to grow and scale, we looked for a paid solution.
  • From a developer resource standpoint, Firebase has been extremely easy to integrate into our app. Whether it be the event tracking, dynamic links or crash reporting we have not had to waste too much developer time thanks to their well-organized developer docs.
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  • In some places, Google BigQuery has helped us save some money by avoiding the need for expensive infrastructure and reducing some of the operational costs.
  • Scalability is up-to-date and really helpful in multiple places.
  • Knowledge transfer is easy as it is very user-friendly, so the learning curve has been reduced.
  • Also, it gives us more insights from our data, helping us make smarter decisions for our business.
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ScreenShots

Google BigQuery Screenshots

Screenshot of Migrating data warehouses to BigQuery - Features a streamlined migration path from Netezza, Oracle, Redshift, Teradata, or Snowflake to BigQuery using the fully managed BigQuery Migration Service.Screenshot of bringing any data into BigQuery - Data files can be uploaded from local sources, Google Drive, or Cloud Storage buckets, using BigQuery Data Transfer Service (DTS), Cloud Data Fusion plugins, by replicating data from relational databases with Datastream for BigQuery, or by leveraging Google's data integration partnerships.Screenshot of generative AI use cases with BigQuery and Gemini models - Data pipelines that blend structured data, unstructured data and generative AI models together can be built to create a new class of analytical applications. BigQuery integrates with Gemini 1.0 Pro using Vertex AI. The Gemini 1.0 Pro model is designed for higher input/output scale and better result quality across a wide range of tasks like text summarization and sentiment analysis. It can be accessed using simple SQL statements or BigQuery’s embedded DataFrame API from right inside the BigQuery console.Screenshot of insights derived from images, documents, and audio files, combined with structured data - Unstructured data represents a large portion of untapped enterprise data. However, it can be challenging to interpret, making it difficult to extract meaningful insights from it. Leveraging the power of BigLake, users can derive insights from images, documents, and audio files using a broad range of AI models including Vertex AI’s vision, document processing, and speech-to-text APIs, open-source TensorFlow Hub models, or custom models.Screenshot of event-driven analysis - Built-in streaming capabilities automatically ingest streaming data and make it immediately available to query. This allows users to make business decisions based on the freshest data. Or Dataflow can be used to enable simplified streaming data pipelines.Screenshot of predicting business outcomes AI/ML - Predictive analytics can be used to streamline operations, boost revenue, and mitigate risk. BigQuery ML democratizes the use of ML by empowering data analysts to build and run models using existing business intelligence tools and spreadsheets.